Quantum machine learning: thinking and exploration in neural network models for quantum science and quantum computing
This book presents a new way of thinking about quantum mechanics and machine learning by merging the two. Quantum mechanics and machine learning may seem theoretically disparate, but their link becomes clear through the density matrix operator which can be readily approximated by neural network mode...
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| Schriftenreihe: | Quantum science and technology
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| Zusammenfassung: | This book presents a new way of thinking about quantum mechanics and machine learning by merging the two. Quantum mechanics and machine learning may seem theoretically disparate, but their link becomes clear through the density matrix operator which can be readily approximated by neural network models, permitting a formulation of quantum physics in which physical observables can be computed via neural networks. As well as demonstrating the natural affinity of quantum physics and machine learning, this viewpoint opens rich possibilities in terms of computation, efficient hardware, and scalability. One can also obtain trainable models to optimize applications and fine-tune theories, such as approximation of the ground state in many body systems, and boosting quantum circuits’ performance. The book begins with the introduction of programming tools and basic concepts of machine learning, with necessary background material from quantum mechanics and quantum information also provided. This enables the basic building blocks, neural network models for vacuum states, to be introduced. The highlights that follow include: non-classical state representations, with squeezers and beam splitters used to implement the primary layers for quantum computing; boson sampling with neural network models; an overview of available quantum computing platforms, their models, and their programming; and neural network models as a variational ansatz for many-body Hamiltonian ground states with applications to Ising machines and solitons. The book emphasizes coding, with many open source examples in Python and TensorFlow, while MATLAB and Mathematica routines clarify and validate proofs. This book is essential reading for graduate students and researchers who want to develop both the requisite physics and coding knowledge to understand the rich interplay of quantum mechanics and machine learning |
| Beschreibung: | Preface 7; 1.1 Outline. 9; I Quantum machine learning and Tensorflow* 11; 2 Introduction 13; 2.1 Fusion between QM and NN. 13; 2.2 The quantum advantage in boson sampling and NN. 13; 2.3 The background of a quantum engineer. 13; 2.4 Impact on the foundation of quantum mechanics. 15; 3 Quantum hardware 17; 4 Review on quantum machine learning and related 19; 4.1 Neural networks in physics beyond quantum mechanics. 20; 4.2 Further readings. 20; 5 Coding fundamentals 21; 5.1 Matrix manipulation in Python. 21; 5.2 What is Tensorflow. 21; 5.3 Tensor and variables in Tensorflow. 21; 5.4 Objects in Tensorflow. 21; 5.5 Models in Tensorflow. 21; 5.5.1 Automatic Graph building. 21; 5.5.2 Automatic differentiation. 21; 6 Neural networks model 23; 6.1 Examples by tensorflow. 23; 7 Reservoir computing 25; 7.1 Examples by tensorflow. 25; II Neural networks and phase space 27; 8 Phase-space representation 29; 8.1 The characteristic function with real variables. 30; 8.2 Gaussian states. 32; 8.3 Vacuum state. 33; 8.4 Coherent state. 33; 9 Linear tran |
| Beschreibung: | xxiii, 378 Seiten Illustrationen, Diagramme 770 gr |
| ISBN: | 9783031442254 |
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| 500 | |a Preface 7; 1.1 Outline. 9; I Quantum machine learning and Tensorflow* 11; 2 Introduction 13; 2.1 Fusion between QM and NN. 13; 2.2 The quantum advantage in boson sampling and NN. 13; 2.3 The background of a quantum engineer. 13; 2.4 Impact on the foundation of quantum mechanics. 15; 3 Quantum hardware 17; 4 Review on quantum machine learning and related 19; 4.1 Neural networks in physics beyond quantum mechanics. 20; 4.2 Further readings. 20; 5 Coding fundamentals 21; 5.1 Matrix manipulation in Python. 21; 5.2 What is Tensorflow. 21; 5.3 Tensor and variables in Tensorflow. 21; 5.4 Objects in Tensorflow. 21; 5.5 Models in Tensorflow. 21; 5.5.1 Automatic Graph building. 21; 5.5.2 Automatic differentiation. 21; 6 Neural networks model 23; 6.1 Examples by tensorflow. 23; 7 Reservoir computing 25; 7.1 Examples by tensorflow. 25; II Neural networks and phase space 27; 8 Phase-space representation 29; 8.1 The characteristic function with real variables. 30; 8.2 Gaussian states. 32; 8.3 Vacuum state. 33; 8.4 Coherent state. 33; 9 Linear tran | ||
| 520 | |a This book presents a new way of thinking about quantum mechanics and machine learning by merging the two. Quantum mechanics and machine learning may seem theoretically disparate, but their link becomes clear through the density matrix operator which can be readily approximated by neural network models, permitting a formulation of quantum physics in which physical observables can be computed via neural networks. As well as demonstrating the natural affinity of quantum physics and machine learning, this viewpoint opens rich possibilities in terms of computation, efficient hardware, and scalability. One can also obtain trainable models to optimize applications and fine-tune theories, such as approximation of the ground state in many body systems, and boosting quantum circuits’ performance. The book begins with the introduction of programming tools and basic concepts of machine learning, with necessary background material from quantum mechanics and quantum information also provided. This enables the basic building blocks, neural network models for vacuum states, to be introduced. The highlights that follow include: non-classical state representations, with squeezers and beam splitters used to implement the primary layers for quantum computing; boson sampling with neural network models; an overview of available quantum computing platforms, their models, and their programming; and neural network models as a variational ansatz for many-body Hamiltonian ground states with applications to Ising machines and solitons. The book emphasizes coding, with many open source examples in Python and TensorFlow, while MATLAB and Mathematica routines clarify and validate proofs. This book is essential reading for graduate students and researchers who want to develop both the requisite physics and coding knowledge to understand the rich interplay of quantum mechanics and machine learning | ||
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Datensatz im Suchindex
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Contents 1 Quantum Mechanics and Data-Driven Physics. Introduction. Quantum Feature Mapping and the Computational Model. Example 1: The First Supervised Learning by Superconducting Qubits. 1.4 Example 2: Photonic Quantum Processors, Boson Sampling, and Quantum Advantage. 1.5 Quantum State Representations. 1.6 Pros and Cons of Quantum Neural Networks. . 1.7 Quantum Mechanics and Kernel Methods. 1.8 More on Kernel Methods. 1.9 Coding Example of Kernel Classification,. 1.10 Support Vector Machine: The Widest Street Approach*. 1.11 The Link of Kernel Machines with the Perceptron. 1.12 Kernel Classification of Nonlinearly Separable Data. 1.13 Feature Mapping. 1.14 The Drawbacks in Kernel Methods. 1.15 Further Reading. References. 1.1 1.2 1.3 2 Kernelizing Quantum
Mechanics. 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 Introduction. The Computational Model by Quantum Feature Maps and Quantum Kernels. Quantum Feature Map by Coherent States. Quantum Classifier by Coherent States. How to Measure Scalar Products with Coherent States. Quantum Feature Map by Squeezed Vacuum. A Quantum Classifier by Squeezed Vacuum. Measuring Scalar Products with General States: The SWAP Test. 40 1 1 2 4 4 6 7 9 12 14 16 21 21 24 26 26 27 29 29 29 31 32 35 37 38 xiii
xiv Contents Boson Sampling and Quantum Kernels. Universality of Quantum Feature Maps and the Reproducing Kernel Theorem* . 2.11 Further Reading. References. 2.9 2.10 3 4 Qubit Maps. 3.1 Introduction. 3.2 Feature Maps with Qubits. 3.3 Using Tensors with Single Qubits. 3.3.1 One-Qubit Gate as a Tensor. 3.4 More on Contravariant and Covariant Tensors in TensorFlow*. 54 3.5 Hadamard Gate as a Tensor. 3.6 Recap on Vectors and Matrices in TensorFlow*. 3.6.1 Matrix-Vector Multiplication as a Contraction. 3.6.2 Lists, Tensors, Row, and Column Vectors. 3.7 One-Qubit Gates. 3.8 Scalar Product. 3.9 Two-Qubit
Tensors. 3.9.1 Further Remarks on the Tensor Indices. 3.10 ΊΧνο-Qubit Gates. 3.10.1 Coding Two-Qubit Gates. 3.10.2 CNOTGate. 3.10.3 CZGate. .’. 3.11 Quantum Feature Maps with a Single Qubit. 3.11.1 Rotation Gates. 3.12 Quantum Feature Maps with Two Qubits. 3.13 Coding the Entangled Feature Map. 3.13.1 The Single Pass Feature Map. 3.13.2 The Iterated Feature Map. 3.14 The Entangled Kernel and Its Measurement with Two Qubits . 3.15 Classical Simulation of the Entangled Kernel. 3.15.1 A Remark on the Computational Time. 3.16 Further Reading. References. One-Qubit Transverse-Field Ising Model and Variational Quantum Algorithms. 85 4.1
Introduction. 4.2 Mapping Combinatorial Optimization to the Ising Model. 4.2.1 Number Partitioning. 4.2.2 Maximum Cut. 4.3 Quadratic Unconstrained Binary Optimization (QUBO). 4.4 Why Use a Quantum Computer for Combinatorial Optimization?. 90 44 46 49 49 51 51 51 52 53 55 56 58 59 61 62 63 64 66 69 70 72 74 75 76 78 79 79 80 80 82 82 83 85 86 86 88 89
Contents XV The Transverse-Field Ising Model. 91 One-Qubit Transverse-Field Ising Model. 92 4.6.1 h = 0. 92 4.6.2 J = 0. 93 4.7 Coding the One-Qubit Transverse-Field Ising Hamiltonian. 93 4.7.1 The Hamiltonian as a tensor. 93 4.7.2 Variational Ansatz as a tensor. 95 4.8 A Neural Network Layer for the Hamiltonian. 97 4.9 Training the One-Qubit Model. 101 4.10 Further Reading. 103 References. 104 4.5 4.6 5 Two-Qubit Transverse-Field Ising Model and Entanglement. 5.1 5.2 Introduction. Two-Qubit Transverse-Field Ising Model. 5.2.1 Ло = Ä1 = 0. 5.2.2 Entanglement in the Ground State with No External Field. 107 5.2.3 Ao = /ii=A^O. 5.3 Entanglement and
Mixtures. 5.4 The Reduced Density Matrix. 5.5 The Partial Trace. 5.6 One-Qubit Density Matrix Using Tensors. 5.7 Coding the One-Qubit Density Matrix. 5.8 Two-Qubit Density Matrix by Tensors. 5.9 Coding the Two-Qubit Density Matrix. 5.10 Partial Trace with tensors. 5.11 Entropy of Entanglement. 5.12 Schmidt Decomposition. 5.13 Entropy of Entanglement with tensors. 5.14 Schmidt Basis with tensors. 5.15 Product States and MaximallyEntangled States with tensors. 5.16 Entanglement in the Two-Qubit TIM. 5.16.1 ho = h and hi = . . 5.17 Further Reading. References. 6 Variational
Algorithms, Quantum Approximate Optimization Algorithm, and Neural Network Quantum States with Two Qubits. 6.1 6.2 6.3 6.4 Introduction . Training the Two-Qubit Transverse-Field Ising Model. Training with Entanglement. The Quantum Approximate Optimization Algorithm. 105 105 105 106 107 108 110 112 115 117 120 122 124 125 126 129 130 134 136 138 139 139 141 141 141 150 154
Contents xvi 7 6.5 Neural Network Quantum States. 6.6 Further Reading. References. 159 167 167 Phase Space Representation. 169 169 170 171 175 Introduction. The Characteristic Function and Operator Ordering. The Characteristic Function in Terms of Real Variables. Gaussian States. 7.4.1 VacuumState. 176 7.4.2 Coherent State. 177 7.4.3 Thermal State. 177 7.4.4 Proof of Eq. (7.42). 178 7.5 Covariance Matrix in Terms of the Derivatives of χ. 7.5.1 Proof of Eqs. (7.72) and (7.73) for General States*. 7.5.2 Proof of Eqs. (7.72) and (7.73) for a Gaussian State*. 7.6 Covariance Matrices and Uncertainties. 7.6.1 The Permuted Covariance Matrix. 184 7.6.2 Ladder Operators and Complex Covariance matrix. 7.7 Gaussian Characteristic
Function. 7.7.1 Remarks on the shape of a Vector. 190 7.8 Linear Transformations and Symplectic Matrices. 7.8.1 Proof of Eqs. (7.141) and (7.142)*. 192 7.9 The U and Μ Matrices*. 7.9.1 Coding the Matrices Rp and Rq. 197 7.10 Generating a Symplectic Matrix for a Random Gate. 7.11 Further Reading. References. 7.1 7.2 7.3 7.4 8 States as a Neural Networks and Gates as Pullbacks. 8.1 Introduction. 8.2 The Simplest Neural Network for a Gaussian State. 8.3 Gaussian Neural Network with Bias Input. 8.4 The Vacuum Layer. 8.5 Building a Model and the “Bug Train”. 8.6 Pullback. 8.7 The Pullback Layer. 8.8 Pullback of Gaussian States. 8.9 Coding the Linear
Layer. 8.10 Pullback Cascading. 8.11 The Glauber Displacement Layer. 8.12 A Neural Network Representation of a Coherent State. 8.13 A Linear Layer for a Random Interferometer. Reference. 179 180 182 182 187 188 191 193 198 199 200 201 201 201 202 205 206 207 208 209 210 211 213 214 216 218
Contents 9 ‘ xvii Quantum Reservoir Computing. 219 Introduction. Observable Quantities as Derivatives of χ. A Coherent State in a Random Interferometer. Training a Complex Medium for an Arbitrary Coherent State . Training to Fit a Target Characteristic Function. Training by First Derivatives. Training by Second Derivatives. 9.7.1 Proof of Eq. (9.5)*. 9.8 Two Trainable Interferometers and a Reservoir. 9.9 Phase Modulator. 9.10 Training Phase Modulators. 9.11 Further Reading. References. 9.1 9.2 9.3 9.4 9.5 9.6 9.7 219 219 221 223 225 228 230 232 233 234 237 238 238 10 Squeezing, Beam Splitters, and Detection. 239 10.1 10.2 10.3 10.4 10.5 10.6 Introduction. The Generalized Symplectic
Operator. Single-Mode Squeezed State. Multimode Squeezed Vacuum Model. Covariance Matrix and Squeezing. Squeezed Coherent States. 10.6.1 Displacing the Squeezed Vacuum. 10.6.2 Squeezing the Displaced Vacuum. 10.7 Two-Mode Squeezing Layer. 10.8 Beam Splitter. 10.9 The Beam Splitter Layer. 10.10 Photon Counting Layer. 10.11 Homodyne Detection. 10.12 Measuring the Expected Value of the Quadrature Operator. References. 239 239 240 241 244 245 245 246 248 252 253 256 260 263 264 11 Uncertainties and Entanglement. 265 265 266 267 268 271 273 276 276 281 281 282 283 287 11.1 11.2 11.3 11.4 11.5 11.6 11.7 11.8 11.9 Introduction. The HeisenbergLayer for General States
. 11.2.1 The LaplacianLayer. 11.2.2 The BiharmonicLayer. 11.2.3 The HeisenbergLayer. Heisenberg Layer for Gaussian States. Testing the HeinsenbergLayer with a Squeezed State. ProofofEqs.(11.4) and (11.5)* and (11.9)*. DifferentialGaussianLayer. Uncertainties in Homodyne Detection. 11.7.1 ProofofEqs.(11.39) and (11.40)*. Uncertainties for Gaussian States. Dif ferentialGaussianLayer on Coherent States.
Contents xviii 12 11.10 DifferentialGaussianLayer in Homodyne Detection . 11.11 Entanglement with Gaussian States. 11.12 Beam Splitters as Entanglers. 11.13 Entanglement by the Reduced Characteristic Function. 11.14 Computing the Entanglement. 11.15 Training the Model to Maximize the Entanglement. 11.16 Further Reading. References. 289 291 292 293 295 298 299 299 Gaussian Boson Sampling . 301 301 302 303 305 306 Introduction. Boson Sampling in a Single Mode. Boson Sampling with Many Modes. Gaussian States. Independent Coherent States. Zero Displacement Case in Complex Variables and the Hafnian. 307 12.6.1 The Resulting Algorithm. 12.6.2 Proof of Eq.
(12.39)*. 12.7 The Q-Transform. 12.8 The Q-Transform Layer. 12.9 The Multiderivative Operator. 12.10 Single-Mode Coherent State. 12.11 Single-Mode Squeezed Vacuum State. 12.12 Multimode Coherent Case. 12.13 A Coherent Mode and a Squeezed Vacuum. 12.14 A Squeezed Mode and a Coherent Mode in a Random Interferometer. 325 12.15 Gaussian Boson Sampling with Haar Random Unitary Matrices. 326 12.15.1 The Haar Random Layer. 12.15.2 A Model with a Varying Number of Layers. 12.15.3 Generating the Sampling Patterns. 12.15.4 Computing the Pattern Probability. 12.16 Training Boson Sampling. 12.17 The Loss Function. 12.18 Trainable GBS Model. 12.19 Boson Sampling the
Model. 12.20 Training the Model. 12.21 Further Reading. References. 12.1 12.2 12.3 12.4 12.5 12.6 13 309 310 311 313 315 317 319 321 323 327 329 330 332 333 335 335 336 341 345 345 Variational Circuits for Quantum Solitons. 347 13.1 13.2 Introduction. The Bose-Hubbard Model. 347 348
Contents xix Ansatz and Quantum Circuit. 13.3.1 Proof of Eq. (13.7)*. 13.3.2 Proof of Eq. (13.8)*. 13.4 Total Number of Particles in Real Variables. 13.5 Kinetic Energy in Real Variables. 13.6 Potential Energy in Real Variables. 13.7 Layer for the Particle Number. . 13.8 Layer for the Kinetic Energy. 13.9 Layer for the Potential Energy. 13.10 Layer for the Total Energy. 13.11 The Trainable Boson Sampling Ansatz. 13.12 Connecting the BSVA to the Measurement Layers . 13.13 Training for the Quantum Solitons. 13.14 Entanglement of the Single Soliton. 13.15 Entangled Bound States of Solitons. 13.16 Boson Sampling. 13.17 Conclusion. 13.18 Further
Reading. References. 349 351 353 353 353 354 354 357 359 360 361 362 363 367 369 371 372 372 373 Index. 375 13.3
This book presents a new way of thinking about quantum mechanics and machine learning by merging the two. Quantum mechanics and machine learning may seem theoretically disparate, but their link becomes clear through the density matrix operator which can be readily approximated by neural network models, permitting a formulation of quantum physics in which physical observables can be computed via neural networks. As well as demonstrating the natural affinity of quantum physics and machine learning, this viewpoint opens rich possibilities in terms of computation, efficient hardware, and scalability. One can also obtain trainable models to optimize applications and fine-tune theories, such as approximation of the ground state in many body systems, and boosting quantum circuits’ performance. The book begins with the introduction of programming tools and basic concepts of machine learning, with necessary background material from quantum mechanics and quantum information also provided. This enables the basic building blocks, neural network models for vacuum states, to be introduced. The highlights that follow include: non-classical state representations, with squeezers and beam splitters used to implement the primary layers for quantum computing; boson sampling with neural network models; an overview of available quantum computing platforms, their models, and their programming; and neural network models as a variational ansatz for many-body Hamiltonian ground states with applications to Ising machines and solitons. The book emphasizes coding, with many open source examples in
Python and TensorFlow, while MATLAB and Mathematica routines clarify and validate proofs. This book is essential reading for graduate students and researchers who want to develop both the requisite physics and coding knowledge to understand the rich interplay of quantum mechanics and machine learning. |
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Quantum mechanics and machine learning may seem theoretically disparate, but their link becomes clear through the density matrix operator which can be readily approximated by neural network models, permitting a formulation of quantum physics in which physical observables can be computed via neural networks. As well as demonstrating the natural affinity of quantum physics and machine learning, this viewpoint opens rich possibilities in terms of computation, efficient hardware, and scalability. One can also obtain trainable models to optimize applications and fine-tune theories, such as approximation of the ground state in many body systems, and boosting quantum circuits’ performance. The book begins with the introduction of programming tools and basic concepts of machine learning, with necessary background material from quantum mechanics and quantum information also provided. This enables the basic building blocks, neural network models for vacuum states, to be introduced. The highlights that follow include: non-classical state representations, with squeezers and beam splitters used to implement the primary layers for quantum computing; boson sampling with neural network models; an overview of available quantum computing platforms, their models, and their programming; and neural network models as a variational ansatz for many-body Hamiltonian ground states with applications to Ising machines and solitons. The book emphasizes coding, with many open source examples in Python and TensorFlow, while MATLAB and Mathematica routines clarify and validate proofs. This book is essential reading for graduate students and researchers who want to develop both the requisite physics and coding knowledge to understand the rich interplay of quantum mechanics and machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">bicssc</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">bicssc</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">bicssc</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">bicssc</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">bicssc</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Quantum computers</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Quantum computing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mathematical physics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer simulation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Neural networks (Computer science) </subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Quantum physics</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Quantencomputer</subfield><subfield code="0">(DE-588)4533372-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Neuronales Netz</subfield><subfield code="0">(DE-588)4226127-2</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Quantenmechanik</subfield><subfield code="0">(DE-588)4047989-4</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Hardcover, Softcover / Physik, Astronomie/Theoretische Physik</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Quantenmechanik</subfield><subfield code="0">(DE-588)4047989-4</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">Quantencomputer</subfield><subfield code="0">(DE-588)4533372-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="3"><subfield code="a">Neuronales Netz</subfield><subfield code="0">(DE-588)4226127-2</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Online-Ausgabe</subfield><subfield code="z">978-3-031-44226-1</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">Digitalisierung UB Passau - ADAM Catalogue Enrichment</subfield><subfield code="q">application/pdf</subfield><subfield code="u">http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034913230&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA</subfield><subfield code="3">Inhaltsverzeichnis</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">Digitalisierung UB Passau - ADAM Catalogue Enrichment</subfield><subfield code="q">application/pdf</subfield><subfield code="u">http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034913230&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA</subfield><subfield code="3">Klappentext</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-034913230</subfield></datafield></record></collection> |
| id | DE-604.BV049567975 |
| illustrated | Illustrated |
| index_date | 2024-07-03T23:29:50Z |
| indexdate | 2024-12-02T11:02:17Z |
| institution | BVB |
| isbn | 9783031442254 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034913230 |
| oclc_num | 1416851933 |
| open_access_boolean | |
| owner | DE-29T DE-739 |
| owner_facet | DE-29T DE-739 |
| physical | xxiii, 378 Seiten Illustrationen, Diagramme 770 gr |
| publishDate | 2024 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| publisher | Springer |
| record_format | marc |
| series2 | Quantum science and technology |
| spelling | Conti, Claudio ca. 20./21. Jh. Verfasser (DE-588)1349466964 aut Quantum machine learning thinking and exploration in neural network models for quantum science and quantum computing Claudio Conti Cham Springer [2024] xxiii, 378 Seiten Illustrationen, Diagramme 770 gr txt rdacontent n rdamedia nc rdacarrier Quantum science and technology Preface 7; 1.1 Outline. 9; I Quantum machine learning and Tensorflow* 11; 2 Introduction 13; 2.1 Fusion between QM and NN. 13; 2.2 The quantum advantage in boson sampling and NN. 13; 2.3 The background of a quantum engineer. 13; 2.4 Impact on the foundation of quantum mechanics. 15; 3 Quantum hardware 17; 4 Review on quantum machine learning and related 19; 4.1 Neural networks in physics beyond quantum mechanics. 20; 4.2 Further readings. 20; 5 Coding fundamentals 21; 5.1 Matrix manipulation in Python. 21; 5.2 What is Tensorflow. 21; 5.3 Tensor and variables in Tensorflow. 21; 5.4 Objects in Tensorflow. 21; 5.5 Models in Tensorflow. 21; 5.5.1 Automatic Graph building. 21; 5.5.2 Automatic differentiation. 21; 6 Neural networks model 23; 6.1 Examples by tensorflow. 23; 7 Reservoir computing 25; 7.1 Examples by tensorflow. 25; II Neural networks and phase space 27; 8 Phase-space representation 29; 8.1 The characteristic function with real variables. 30; 8.2 Gaussian states. 32; 8.3 Vacuum state. 33; 8.4 Coherent state. 33; 9 Linear tran This book presents a new way of thinking about quantum mechanics and machine learning by merging the two. Quantum mechanics and machine learning may seem theoretically disparate, but their link becomes clear through the density matrix operator which can be readily approximated by neural network models, permitting a formulation of quantum physics in which physical observables can be computed via neural networks. As well as demonstrating the natural affinity of quantum physics and machine learning, this viewpoint opens rich possibilities in terms of computation, efficient hardware, and scalability. One can also obtain trainable models to optimize applications and fine-tune theories, such as approximation of the ground state in many body systems, and boosting quantum circuits’ performance. The book begins with the introduction of programming tools and basic concepts of machine learning, with necessary background material from quantum mechanics and quantum information also provided. This enables the basic building blocks, neural network models for vacuum states, to be introduced. The highlights that follow include: non-classical state representations, with squeezers and beam splitters used to implement the primary layers for quantum computing; boson sampling with neural network models; an overview of available quantum computing platforms, their models, and their programming; and neural network models as a variational ansatz for many-body Hamiltonian ground states with applications to Ising machines and solitons. The book emphasizes coding, with many open source examples in Python and TensorFlow, while MATLAB and Mathematica routines clarify and validate proofs. This book is essential reading for graduate students and researchers who want to develop both the requisite physics and coding knowledge to understand the rich interplay of quantum mechanics and machine learning bicssc bisacsh Machine learning Quantum computers Quantum computing Mathematical physics Computer simulation Neural networks (Computer science) Quantum physics Quantencomputer (DE-588)4533372-5 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Quantenmechanik (DE-588)4047989-4 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Hardcover, Softcover / Physik, Astronomie/Theoretische Physik Quantenmechanik (DE-588)4047989-4 s Maschinelles Lernen (DE-588)4193754-5 s Quantencomputer (DE-588)4533372-5 s Neuronales Netz (DE-588)4226127-2 s DE-604 Erscheint auch als Online-Ausgabe 978-3-031-44226-1 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034913230&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034913230&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Conti, Claudio ca. 20./21. Jh Quantum machine learning thinking and exploration in neural network models for quantum science and quantum computing bicssc bisacsh Machine learning Quantum computers Quantum computing Mathematical physics Computer simulation Neural networks (Computer science) Quantum physics Quantencomputer (DE-588)4533372-5 gnd Neuronales Netz (DE-588)4226127-2 gnd Quantenmechanik (DE-588)4047989-4 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4533372-5 (DE-588)4226127-2 (DE-588)4047989-4 (DE-588)4193754-5 |
| title | Quantum machine learning thinking and exploration in neural network models for quantum science and quantum computing |
| title_auth | Quantum machine learning thinking and exploration in neural network models for quantum science and quantum computing |
| title_exact_search | Quantum machine learning thinking and exploration in neural network models for quantum science and quantum computing |
| title_exact_search_txtP | Quantum machine learning thinking and exploration in neural network models for quantum science and quantum computing |
| title_full | Quantum machine learning thinking and exploration in neural network models for quantum science and quantum computing Claudio Conti |
| title_fullStr | Quantum machine learning thinking and exploration in neural network models for quantum science and quantum computing Claudio Conti |
| title_full_unstemmed | Quantum machine learning thinking and exploration in neural network models for quantum science and quantum computing Claudio Conti |
| title_short | Quantum machine learning |
| title_sort | quantum machine learning thinking and exploration in neural network models for quantum science and quantum computing |
| title_sub | thinking and exploration in neural network models for quantum science and quantum computing |
| topic | bicssc bisacsh Machine learning Quantum computers Quantum computing Mathematical physics Computer simulation Neural networks (Computer science) Quantum physics Quantencomputer (DE-588)4533372-5 gnd Neuronales Netz (DE-588)4226127-2 gnd Quantenmechanik (DE-588)4047989-4 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | bicssc bisacsh Machine learning Quantum computers Quantum computing Mathematical physics Computer simulation Neural networks (Computer science) Quantum physics Quantencomputer Neuronales Netz Quantenmechanik Maschinelles Lernen |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034913230&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034913230&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
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